Congratulations to the 16 papers below selected for spotlight presentations!
These will be presented in rapid succession (2 min. each) during the 9:45 - 10:15 slot of our workshop program to give our audience a taste of exciting work happening in the ML+Health space.
What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems
Owen Lahav, Nicholas Mastronarde and Mihaela van der Schaar
Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning
Changhee Lee, Nicholas Mastronarde and Mihaela van der Schaar
MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks
Daniel Jarrett, Jinsung Yoon and Mihaela van der Schaar
Model-Based Reinforcement Learning for Sepsis Treatment
Aniruddh Raghu, Matthieu Komorowski and Sumeetpal Singh
Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential
Sandesh Ghimire, Jwala Dhamala, Prashnna Kumar Gyawali and Linwei Wang
Radiotherapy Target Contouring with Convolutional Gated Graph Neural Network
Chun-Hung Chao, Yen-Chi Cheng, Hsien-Tzu Cheng, Chi-Wen Huang, Tsung-Ying Ho, Chen-Kan Tseng, Le Lu and Min Sun
DeepSPINE: automated lumbar spinal stenosis grading using deep learning
Jen-Tang Lu, Stefano Pedemonte, Bernardo Bizzo, Sean Doyle, Katherine Andriole, Mark Michalski, R, Gilberto Gonzalez and Stuart Pomerantz
Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images
Edward Chou, Matthew Tan, Cherry Zou, Michelle Guo, Albert Haque, Arnold Milstein and Li Fei-Fei
Deep Learning with Attention to Predict Gestational Age of the Fetal Brain
Liyue Shen, Edward Lee, Katie Shpanskaya and Kristen Yeom
Registration of Sparse Clinical Images
Kathleen Lewis, Guha Balakrishnan, John Guttag and Adrian Dalca
Improving Clinical Predictions through Unsupervised Time Series Representation Learning
Xinrui Lyu, Matthias Hüser, Stephanie Hyland, George Zerveas and Gunnar Rätsch
Multiple Instance Learning for ECG Risk Stratification
Divya Shanmugam, Davis Blalock and John Guttag
Inferring Multidimensional Rates of Aging from Cross-Sectional Data
Emma Pierson, Pang Wei Koh, Tatsunori Hashimoto, Daphne Koller, Jure Leskovec, Nick Eriksson and Percy Liang
Using permutations to assess confounding in machine learning applications for digital health
Elias Chaibub Neto, Abhishek Pratap, Thanneer Perumal, Meghasyam Tummalacherla, Brian Bot, Lara Mangravite and Larsson Omberg
Deep Sequence Modeling for Hemorrhage Diagnosis
Fabian Falck, Michael Pinsky and Artur Dubrawski
Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records
Xiangan Liu, Keyang Xu, Pengtao Xie and Eric Xing